The elastic net algorithm and protein structure prediction

Journal of Computational Chemistry
Keith D BallKen A Dill

Abstract

Predicting protein structures from their amino acid sequences is a problem of global optimization. Global optima (native structures) are often sought using stochastic sampling methods such as Monte Carlo or molecular dynamics, but these methods are slow. In contrast, there are fast deterministic methods that find near-optimal solutions of well-known global optimization problems such as the traveling salesman problem (TSP). But fast TSP strategies have yet to be applied to protein folding, because of fundamental differences in the two types of problems. Here, we show how protein folding can be framed in terms of the TSP, to which we apply a variation of the Durbin-Willshaw elastic net optimization strategy. We illustrate using a simple model of proteins with database-derived statistical potentials and predicted secondary structure restraints. This optimization strategy can be applied to many different models and potential functions, and can readily incorporate experimental restraint information. It is also fast; with the simple model used here, the method finds structures that are within 5-6 A all-Calpha-atom RMSD of the known native structures for 40-mers in about 8 s on a PC; 100-mers take about 20 s. The computer time tau sca...Continue Reading

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Citations

Sep 27, 2002·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Berrin Yanikoglu, Burak Erman
Apr 27, 2005·Journal of Bioinformatics and Computational Biology·Xiang-Sun ZhangLuonan Chen
Sep 29, 2011·Neural Computing & Applications·Marcos Lévano, Hans Nowak
Oct 17, 2009·Journal of Theoretical Biology·Sourav Rakshit, G K Ananthasuresh
Sep 20, 2006·Journal of Biochemical and Biophysical Methods·Yu-Zhen GuoYong Wang
Jul 15, 2015·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Yuzhen Guo

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